Improving junior doctor medicine prescribing and patient safety: an intervention using

personalised, structured, video-enhanced feedback and deliberate practice

William Green1,a, Muhammad Waseem Shahzad1, Stephen Wood1, Maria Martinez Martinez2,

Andrew Baines3, Ahmad Navid4, Robert Jay5, Zara Whysall6, John Sandars7, Rakesh Patel5

a. Corresponding author

University of Leicester, Leicester LE1 7RH

w.green@leicester.ac.uk

Author affiliations

1. University of Leicester School of Business, University of Leicester, Leicester LE1

7RH, UK

2. Leicester General Hospital, University Hospitals of Leicester (UHL) NHS Trust,

Leicester LE5 4PW, UK

3. Pilgrim Hospital Boston, United Lincolnshire Hospitals (ULH) NHS Trust, Boston,

Lincolnshire PE21 9QS, UK

4. Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS

Foundation Trust, Edgbaston, Birmingham B15 2GW, UK

5. School of Medicine, University of Nottingham, Nottingham, NG7 2UH, UK

6. Department of Human Resource Management, Nottingham Business School,

Nottingham Trent University, Nottingham NG1 4FQ, UK

7. Health Research Institute, Faculty of Health, Social Care and Medicine, Edge Hill

University, Ormskirk, Lancashire L39 4QP, UK

b. [Principal Investigator statement] The authors confirm that the Principal Investigators for

this paper are RSP & WG.

ii. [running header] An intervention to improve junior doctor prescribing

1

iii. [keywords] Avoidable Harm, Deliberate Practice, Foundation Training, Junior

Doctors, Patient Safety, Prescribing, Video-Enhanced Feedback.

Word count: 4617

Table count: 10 (4 in main paper, 6 in supplementary file)

Figure count: 6 (4 in main paper, 2 in supplementary file)

What is already known about this subject

1. Junior doctors in their Foundation Training are more likely to make prescribing errors than

experienced healthcare professionals.

2. Educational interventions are inconsistent at reducing prescribing errors among new

prescribers such as Foundation Year doctors.

3. The effectiveness of simulation-based interventions for improving prescribing in practice is

limited.

What this study adds

1. Simulated clinical encounters with personalised, structured, video-enhanced feedback

using a deliberate practice approach support Foundation Year doctors to prescribe at a level

consistent to experienced healthcare professionals.

2. The intervention was implemented with consistent findings across different clinical sub-

specialty contexts in medicine (nephrology and renal transplantation) and surgery (general

and orthopaedics).

3. The intervention is cost effective and patients who were prescribed medication by

Foundation Years who did not receive the intervention experienced significantly higher

prescribing error rates.

2

Improving junior doctor medicine prescribing and patient safety: an intervention using

personalised, structured, video-enhanced feedback and deliberate practice

ABSTRACT

Aim

This research investigated the effectiveness of an intervention for improving the prescribing

and patient safety behaviour of Foundation Year doctors. The intervention consisted of

simulated clinical encounters delivered over a two-hour period using personalised, structured,

video-enhanced feedback and deliberate practice, undertaken at the start of four-month sub-

specialty rotations.

Method

Three prospective, non-randomised control intervention studies were conducted, within two

secondary care NHS Trusts in England. The primary outcome measure, error rate per

prescriber, was calculated using daily prescribing data. Prescribers were grouped to enable a

comparison between experimental and control conditions using regression analysis. A break-

even analysis evaluated cost effectiveness.

Results

There was no significant difference in error rates of novice prescribers who received the

intervention when compared with those of experienced prescribers. Novice prescribers not

participating in the intervention had significantly higher error rates (p=0.026, 95% CI Wald

0.093 to 1.436; p=0.026, 95% CI 0.031 to 0.397) and patients seen by them experienced

significantly higher prescribing error rates (p=0.007, 95% CI 0.025 to 0.157). Conversely,

3

patients seen by the novice prescribers who received the intervention experienced a

significantly lower rate of significant errors compared to patients seen by the experienced

prescribers (p=0.04, 95% CI -0.068 to -0.001). The break-even analysis demonstrates cost-

effectiveness for the intervention.

Conclusion

Simulated clinical encounters using personalised, structured, video-enhanced feedback and

deliberate practice improves the prescribing and patient safety behaviour of junior doctors in

their Foundation Training. The intervention is cost-effective with potential to reduce

avoidable harm.

4

INTRODUCTION

“Unsafe medication practices and medication errors are a leading cause of avoidable harm in

health care systems across the world”[1]. In response to the rise in the proportion of

medicine-related deaths and patient safety risks, the World Health Organization (WHO)

identified and announced “Medication without harm” as the third challenge faced by the

World Alliance for Patient Safety in 2017.[2] In England medication errors due to adverse

drug reactions are significant, consuming 181,626 bed days, causing 712 deaths and

contributing to 1,708 deaths in a year.[3]

As part of the effort to overcome medication-related errors, one approach is to focus on

prescribing. Prescribing is associated with medical errors, with high error rates across the

world[4-8] despite arguments of universal underreporting.[9] Prescribing medication is an

essential and complex skill[10] for an increasingly wide range of prescribers including

doctors, nurses and other prescribers. Prescribing involves the initiation, monitoring,

continuation and modification of medication therapy, demanding a thorough understanding of

clinical pharmacology as well as the judgement and ability to prescribe rationally for the

benefit of patients.[11] Against this backdrop a prescribing error has been defined as “an

unintentional significant (1) reduction in the probability of treatment being timely and

effective or (2) increase in the risk of harm when compared with generally accepted

practice”.[12, p. 235]

Reducing avoidable harm from prescribing errors made by healthcare professionals should be

a priority particularly among Foundation Year doctors (junior doctors in their first and second

year of hospital training) who are known to be more likely to maker error.[ 6, 13] This group

were found to make more prescribing errors than experienced colleagues,[6] even accounting

5

for the fact they prescribe more items in proportion to other grades and types of healthcare

professionals. The Prescribing Safety Assessment (PSA) was introduced in 2017 for medical

students before graduating as a Foundation Year doctor,[14] to minimise the contribution of

knowledge deficits towards prescribing errors made in practice. Whilst interventions like the

PSA[14] ensure individuals have a minimum standard for some aspects related to prescribing,

the longer-term impact of the assessment on improving prescribing behaviour over time when

individuals enter practice remains unknown.

The design and assessment of interventions for reducing prescribing errors among

Foundation Year doctors needs to take into account the causal factors including the work

environment (workload and time pressure), team factors (multiple individuals’ involvement,

communication, medicines reconciliation and documentation following incorrect

instructions), task factors (poor availability of drug information at the time of admission, and

support systems not available), individual factors (lack of personal knowledge and

experience), and patient-related factors (complexity of symptoms).[6, 15-20] This evidence

also confirms that the causes of prescribing errors are multifactorial and multi-level, which is

consistent with current understanding about human factors and error more generally across

many safety-critical industries.[21] Previous systematic reviews evaluating the effectiveness

of prescribing interventions have been inconclusive,[22-24] probably because many of the

studies appeared only to focus on a single target for interventions such as increasing

prescribing knowledge. Consequently, the authors of these reviews have repeatedly called on

further research to address the multiple factors identified from studies on human error, and

develop more sophisticated and multi-dimensional interventions that can address these

contributing factors.[ 6, 15-20]

6

Closer examination of some of the empirical studies included in these reviews identifies a

number of methodological concerns. Only 19% of studies in one of the reviews distinguished

between different grades of medical prescribers,[22] therefore identifying what works for

novice prescribers such as Foundation Year doctors remains unclear. Another review[23] also

confirmed only 13% focusing on new prescribers, meaning clear differences in the

effectiveness of particular types or combinations of interventions cannot be deciphered for

different grades or experience of prescriber. Many of these studies investigate the

effectiveness of interventions in single prescribing contexts rather than across a range,

limiting the transferability of the interventions. These single site studies typically involve pre-

and post-test interventions[23] despite some reported limitations.[24]

Developing expertise through deliberate practice and feedback

Deliberate practice is an instructional approach to developing expertise where the goal is to

develop mental models in the minds of learners for how they go about the planning,

execution, monitoring and analysis of complex tasks such as prescribing.[25, 26] There are

examples of educational interventions underpinned by deliberate practice with particular

effectiveness in terms of improved learning outcomes across a range of academic learning

tasks[27] and clinical skills.[28, 29] A Best Evidence in Medical Education (BEME)

systematic review of high-fidelity simulations spanning over three decades of research

identified that learning outcomes that adopted deliberate practice were mixed.[29] However,

all were positive when feedback was provided as a structured activity alongside.[29] The

timing of feedback following performance on clinical simulations underpinned by deliberate

practice is known to be important[30] as well as the way in which it is given, that is,

structured rather than informal or lacking a framework.[31, 32]

7

The way that feedback is delivered, that is, the medium through which it is being

communicated is also important for improving potential outcomes.[33] Video feedback has

been demonstrated to have a positive performance effect across a wide range of areas – sport,

music, communication and rehabilitation.[34-37] Within healthcare the use of video feedback

has already been used in improving prescribing, medical and surgical outcomes.[38-44] A

study involving novices[45] identified that video-enhanced feedback improved clinical skills

performance over and above the effect of receiving feedback on skills development directly

from an expert. In another study involving pharmacist-led video-stimulated feedback

intervention, researchers also observed a reduction in prescribing errors among participants

albeit there was no control group to fully assess the real effect of the intervention.[46]

The aim of this research was to investigate the effectiveness of simulated clinical encounters

using personalised, structured, video-enhanced feedback and deliberate practice for

improving the prescribing and patient safety behaviour of junior doctors in their Foundation

Training.

METHODS

Our intervention (described in Appendix A) combined the simulation of a clinical encounter

and the use of personalised, structured, video-enhanced feedback. To facilitate deliberate

practice, specific elements of practice for the doctors to focus on were identified through

video viewing. The design of the simulated clinical encounters and simulation environment

resembled an actual clinical environment in order to capture the authentic range of factors

that contribute towards prescribing errors.[6, 14-20, 23]

Study sites

8

Three prospective, non-randomised intervention studies were conducted across four-month

rotations in Nephrology and Renal Transplantation (Study 1), Surgery (Study 2) and

Orthopaedics (Study 3) in two acute care NHS Trusts in England. Junior doctors on a

Foundation and Core Training programme in the UK rotate through a different specialty

every four months for two years. Experimental groups included ‘novice’ junior doctors,

defined as Foundation Year doctors; and those in their first and second year of training, on

the East Midlands South Foundation Training and Core Medical Training programmes.[47]

Control groups comprised other prescribers (novices and experienced) working on the study

wards but not participating in the intervention. Experienced prescribers comprised those who

had completed their Foundation Training. The simulated clinical encounters were delivered

over two days in the first week of a four-month rotation. The personalised, structured, video-

enhanced feedback on prescribing behaviour was completed within the first month of the

four-month rotation.

Study design

A consistent approach to participant recruitment was adopted across the three study sites.

Participants were invited to participate on a voluntary basis via email from the lead

pharmacists (authors 4 and 5). The experimental groups were made up of cohorts rotating

through the study sub-specialty sites.

Study 1

This study had two objectives. First, to assess the value of personalised, structured, video-

enhanced feedback over and above any learning gained from participating in simulated

clinical encounters alone. Second, to establish the cost-effectiveness of the intervention on

reducing avoidable harm. Two experimental groups and two control groups were constructed:

9

Experimental Group 1 – Foundation Year prescribers who participated in simulated clinical

encounters; Experimental Group 2 – Foundation Year prescribers who participated in

simulated clinical encounters and received personalised, structured, video-enhanced

feedback; Novice Control – Foundation Year prescribers in their first and second year of

practice; and Experienced Control – experienced prescribers beyond Foundation Years. To

mitigate the knowledge-sharing effect between the experimental groups, Experimental Group

1 participated in the rotation prior to Experimental Group 2.

Junior doctors rotating to the Department of Nephrology and Renal Transplantation, John

Walls Renal Unit, Leicester General Hospital, Universities of Leicester NHS Trust, were

invited to participate in the study. The department includes four inpatient wards with a total

of 59 beds, with all patients admitted under specialist renal care. Simulated clinical

encounters were conducted at the Robert Kilpatrick Clinical Sciences Building, Leicester

Royal Infirmary, Universities of Leicester NHS Trust.

Study 2

This study had one objective: to assess the prescribing behaviour of Foundation Year doctors

following participation in the intervention at a second research site. The intervention was

delivered following the effective demonstration of Experimental Group 2 in Study 1. An

Experimental Group of Foundation Year doctors who participated in simulated clinical

encounters and received personalised, structured, video-enhanced feedback was compared to

an Experienced Control group of experienced prescribers beyond Foundation Years.

Foundation Year doctors rotating to the Department of Surgery were invited to participate in

the study. The department included two inpatient wards with a total of 29 beds. Simulated

10

clinical encounters were conducted in an area of the Discharge Lounge not used for patient

care at Pilgrim Hospital, United Lincolnshire Hospitals NHS Trust.

Study 3

This study had two objectives. First to assess the prescribing behaviour of Foundation Year

doctors following participation in the intervention at a third research site. The second

objective sought to establish the impact of the participating doctors prescribing behaviour on

patients, as one patient is likely to be prescribed items by multiple prescribers. Prescribing

data were, therefore, analysed to compare prescribing error rates experienced by patients

dependent on whether the individual items prescribed had been written by a prescriber

belonging to a particular study group (denoted as Patient level data analysis).

To meet these two objectives, Study 3 repeated the intervention in the same hospital as Study

2 but in the Department of Orthopaedics. Junior doctors rotating to this department were

invited to participate in the study. Data were compared across three groups: an Experimental

Group of Foundation Year doctors who participated in simulated clinical encounters and

received personalised, structured, video-enhanced feedback; a Novice Control group of

Foundation Year doctors; and an Experienced Control group of doctors beyond Foundation

Year. The department included one elective surgery ward with 14 beds and one trauma ward

with 28 beds.

Data collection

Medication orders across the three study sites were made on written prescriptions. Data were

collected over a four-month rotation period for all prescribers. Pharmacists responsible for

the inpatient wards participating in the studies collected data from inpatient drug charts and

11

were blinded to a prescriber’s experimental condition. All doctors working in the study sites

over the duration of the research were aware prescribing was monitored by pharmacists as

part of the usual medicines reconciliation process.

A standardised prescribing error data collection form (Appendix B) was designed and piloted

prior to Study 1 to ensure consistency across all sites.[12] Discharge data were not included,

similar to other research investigating written prescribing errors.[6] The form required the

pharmacy team to record the date a prescription was made, ward, patient details (initials and

hospital number), prescriber details (initials, occupation and grade) and prescribing error

details (drug name, dose and frequency, description of error, what, if any, doses were given,

whether the error led to actual negative outcomes for the patient and the potential severity of

error). The potential severity of error classification system used in the GMC funded EQUIP

study,[6] which was based on prior research,[48-52] was also used for this research. This

classification system uses four severities of error: minor, significant, serious and potentially

lethal.

Data collection was conducted as part of the pharmacist’s daily activity and therefore errors

were also corrected as part of their usual medicine reconciliation process. All patient

prescription charts and all prescribed medicine items were reviewed. All data collected was

peer reviewed by the lead pharmacist at each study site. Anonymised patient level data were

shared by the lead pharmacist with the study team for analysis.

Data analysis

The independent variable in Study 1 measured group membership, differentiating between

four groups: Experimental Group 1, Experimental Group 2, Novice Control Group and

12

Experienced Control Group. The dependent variable was a count variable, number of errors

per prescriber. The data were analysed using a negative binomial regression (to adjust for

over dispersion of data) using the Experienced Control Group as the reference group. A

break-even analysis was conducted to establish the cost effectiveness of the intervention

based on Study 1. The break-even analysis is detailed in Appendix C.

The independent variable in Study 2 consisted of two groups: Experimental Group and

Experienced Control Group. The dependent variable in Study 2 was the error rate per

prescriber, calculated as the number of errors divided by total number of items prescribed by

each individual prescriber. Therefore these data accounted for the total number of items

prescribed by all prescribers.

The independent variable in Study 3 consisted of three groups: Experimental Group, Novice

Control Group and Experienced Control Group. The dependent variable in Study 3 was, as in

Study 2, the error rate per prescriber, calculated as the number of errors divided by total

number of items prescribed by each individual. Again, these data accounted for the total

number of items prescribed by all prescribers.

Study 2 and 3 data were analysed using regression analysis in STATA 15 using the

Experienced Control Group as the reference group. Given the experimental nature of the

study design these analyses enable causal links to be proposed,[53] however, causal links

must be approached with caution due to the limitations of the non-randomized study design.

Data were further analysed to establish the impact on patients of prescribing behaviours

demonstrated by the three groups on the patients in Study 3. For this specific analysis, the

13

dependent variable was error rate per patient, calculated as the number of errors (in items

prescribed to each individual patient) divided by total number of items prescribed to each

individual patient. The independent variable consisted of three groups (Experimental Group,

Novice Control and Experienced Control) with the relevant number of patients seen by

participants in each group. These data were analysed using regression analysis using the

Experienced Control Group as the reference group.

Ethics

The study was undertaken and registered at the two NHS Trusts as part of their patient safety

and improving quality clinical effectiveness programme, and Health Education England

working across the East Midlands wider quality improvement and innovation initiative (study

reference number LEI0085). The study did not require full NHS ethics approval.

Patient and public involvement

Patients affiliated to the Leicester Kidney Patient Association were invited to support Study

1. Patients were identified and invited by clinical leads to support Studies 2 and 3. All

patients co-designed and co-delivered the simulated clinical encounters across all three study

sites. All patients were involved in the local dissemination of outcomes from each study and

the findings across all sites were presented by the lead author to the East Midlands Patient

and Public Involvement Senate.

RESULTS

Study 1: Department of Nephrology and Renal Transplantation

Participants

14

All junior doctors invited to the simulations participated: eleven without video-enhanced

feedback (Experimental Group 1: seven FY1 and four FY2) and thirteen with video-enhanced

feedback (Experimental Group 2: eight FY1 and five FY2). Five participants were excluded

from analysis as no prescribing data were attributable to them; two FY1 and two FY2 from

Experimental Group 1 and one FY2 from Experimental Group 2 (see Table 1).

< TABLE 1 HERE >

Prescribing data

There was a significant difference in error rates between Experimental Group 1, the

intervention group who did not receive video-enhanced feedback, and Experienced Control

(p=0.006, 95% CI Wald 3.36 to 2.034). Experimental Group 1 had the highest number of

errors (14.57) per prescriber amongst all four groups (Table and Figure 1). However, there

was no significant difference in error rates between Experimental Group 2, the intervention

group who did receive the video-enhanced feedback, and Experienced Control. There was a

significantly higher number of errors per prescriber (9.57) among the Novice Control group

compared to the Experienced Control group (p=0.082, 95% CI Wald -0.096 to 1.625) (Table

and Figure 1).

< FIGURE 1 HERE >

Experimental Group 1 also made significantly more errors for both minor and significant

error categories, compared with Experienced Control (p=0.015, 95% CI Wald 1.24 to 7.52;

and p=0.005, 95% CI Wald 1.45 to 8.52 respectively). There was no significant difference in

errors made between Experimental Group 2, Novice Control and Experienced Control across

both categories of error type.

15

Novice Control had significantly higher serious errors per prescriber compared to

Experienced Control (p<0.001, 95% CI Wald 3.61 to 13.58). There was no significant

difference in serious error rates between Experimental Group 2 and Experienced Control.

The break-even analysis (Appendix C) calculated from the errors observed in the data

demonstrates that the cost of the intervention is less than the potential cost attributed to the

observed errors.

Study 2: Department of Surgery

Participants

Fourteen junior doctors were invited and participated in the simulations and were included in

the analysis (see Table 2).

< TABLE 2 HERE >

Prescribing data

There was no significant difference in error rates per prescriber between the Experimental

Group comprising Foundation Year doctors and Experienced Control comprising experienced

prescribers beyond Foundation Year (Table and Figure 2). This finding was observed in spite

of a significantly higher level of prescribing activity for the Experimental Group (p<0.001,

95% CI 322.63 to 526.99). There was no significant difference in error rates per prescriber

across the various error severity types.

< FIGURE 2 HERE >

16

Study 3: Department of Orthopaedics

Participants

The twelve junior doctors rotating through the Orthopaedic specialty were invited to

participate. Seven were able to attend the simulations and constitute the Experimental Group.

The remaining five could not participate due to clinical commitments and constitute the

Novice Control (see Table 3).

< TABLE 3 HERE >

Prescribing data

There was a significantly higher error rate per prescriber among the Novice Control group

compared to the Experienced Control group (p=0.026, 95% CI 0.031 to 0.397; f=1.11) (Table

and Figure 3). However, there was no significant difference in error rate per prescriber

between the Experimental Group and Experienced Control group.

< FIGURE 3 HERE >

There was also no significant difference in error rate per prescriber across the various grades

of error severity type between the Experimental group and Experienced Control group.

However, there was a significantly higher minor error rate per prescriber among the Novice

Control group compared to the Experienced Control group (p=0.021, 95% CI 0.041 to 0.413,

f=1.10).

Patient level data

Patients prescribed medicines by the Novice Control group had a significantly higher error

rate per patient (28.38%) than patients prescribed medicines by the Experienced Control

17

group (19.29%) (p=0.007, 95% CI 0.025 to 0.157 f=0.12) (Table and Figure 4). There was no

significant difference in error rates for patients prescribed medicines by the Experimental

Group and Experienced Control group. Patients prescribed medicines by the Experimental

Group had the lowest error rate per patient compared with other groups (14.01%).

< TABLE 4 HERE >

< FIGURE 4 HERE >

The same trend was also observed for minor error rates with patients seen by the Novice

Control group subject to the most minor errors in comparison to patients seen by both other

groups. Likewise, patients seen by the Experimental Group were subject to the fewest minor

errors made in comparison to the patients seen by both other groups. Conversely, patients

seen by the Experimental Group had a significantly lower rate of significant errors per patient

(2.43%) compared to patients seen by the Experienced Control group (p=0.04, 95% CI -0.068

to -0.001; f=0.12). There was no significant difference in error rates among patients seen by

the Novice Control group and Experienced Control group.

DISCUSSION

This research demonstrated that simulated clinical encounters and personalised, structured,

video-enhanced feedback using a deliberate practice approach is an effective intervention for

improving the prescribing behaviour of Foundation Year doctors on four-month training

rotations across two NHS Trusts in the UK. The intervention was effective at reducing

potential avoidable harm to patients, given that patients seen by the novice prescribers

participating in the intervention experienced lower prescribing errors rates. This research

addressed some of the concerns and gaps previously identified related to prescribing

interventions [20-24, 54] by demonstrating the impact of the intervention for improving

18

clinical outcomes,[55] namely, reducing prescribing errors rates, as well as confirming the

transferability of the intervention across different clinical sub-specialty contexts. Finally, the

research suggested the intervention was cost-effective for reducing the potential harm caused

from prescribing errors thereby going some way toward helpfully attaining the WHO

“Medication with Harm” goal in the UK context.

Previous research has consistently identified that all prescribers make errors irrespective of

experience, grade or professional background, but Foundation Year doctors make more

compared to other prescriber groups.[6, 12] The findings in this research also confirmed the

same observation. This is the first study, however, to propose and demonstrate an effective

intervention for improving the prescribing behaviour of novice prescribers to the level of

experienced prescribers. In addition, this research suggests educational interventions (in our

case personalised, structured and video-enhanced feedback) underpinned by a deliberate

practice approach are effective in changing and sustaining prescribing behaviours over at

least a four-month period in comparison to education that merely seeks to provide ‘a training

experience’ or promote a change in knowledge alone.

The three-study research design was specifically constructed to address concerns raised in

educational, training and patient safety research in relation to a lack of evidence for

interventions in real-world settings.[55, 56] This research demonstrates that complex

interventions designed to improve medical education, patient safety and practice can be

operationalised in naturalistic healthcare settings without significant difficulty, whilst

adopting a robust, experimental study design. That said, the findings from this research need

to be investigated in other clinical contexts using a multicentre randomised study design to

critically investigate the reproducibility of the intervention.

19

The impact of feedback on performance is widely reported across many domains and

disciplines.[30] However, the findings from this research raise a number of interesting issues

about feedback given to Foundation Year doctors ranging from, first, practical issues related

to method and timing, through to, second, philosophical considerations related to educational

approach and ethics. First, Foundation Year doctors do not currently consistently receive

personalised, structured feedback following simulation activities organised as part of their

postgraduate training. This research demonstrated that the absence of such feedback may, at a

minimum, have no effect, or, in extreme cases, could lead to worsening future prescribing

behaviour in practice and a risk of patient harm. Second, factors such as low self-confidence,

inexperience in practice and fear of not appearing knowledgeable, may also impact the

effectiveness of feedback among learners.[57] This should be taken into account when

delivering this or similar interventions in the future.

Previous research on prescribing interventions incorporating feedback do not demonstrate

consistent impact on outcomes.[29, 58] This suggests medical educators (or anyone

delivering prescribing interventions) should not assume interventions delivered together with

the provision of general feedback are enough to improve prescribing behaviour among junior

doctors. The effect of video in facilitating feedback and deliberate practice,[59] however, has

been found to be particularly important for learning gains in a variety of contexts.[30] In

healthcare, two studies have successfully demonstrated the adoption of video-based feedback;

first, to improve surgical technical skills[60] and, second, to reduce prescribing errors.[46]

These two interventions involved filming practice in situ. Whilst our research designed

simulated clinical encounters with ‘real’ patients, there remains a need for evaluating the

feasibility of implementing interventions that go beyond technical skills on a greater scale.

20

The findings from this research have implications for the delivery of simulated encounters for

Foundation Training and also simulations delivered as part of undergraduate medical

curricula. Currently this type of training has greater emphasis on the ‘overlearning’ of

technical or psychomotor skills, such as advanced life support, rather than facilitating

complex cognitive skills such as prescribing, which involves complicated problem-solving

and diagnostic decision-making. Whilst training and assessment of competence in these skills

are important among Foundation Year doctors, more simulation time should be given to

improving ‘everyday skills’ such as prescribing, safe handover,[61] acting on results,[62] and

undertaking ward rounds.[63]

The observed error rates calculated from individual’s daily prescribing data, collected in

Study 2 and 3, are notably greater than the group level error rates reported in the most cited

previous study[6]. There are four reasons attributed to this which suggest comparisons with

other research should be done with caution. First, error rate data in this research is based on

individual’s prescribing activity, whilst previous research[6] reports group level prescribing

activity. Second, the underpinning methodology for collecting the baseline prescribing data

was unique in our research. Data was collected continuously over a four-month period across

all studies, whereas a sampling day approach is a more conventional method of data

collection.[6] Third, the sampling approach taken to collect data in the most cited previous

study[6] was conducted across 19 acute hospital trusts in the North-West region of England

but data for individual trusts were not reported. The research reported in this manuscript

reports on specific sites in the Midlands region of England; geographical variation across

study sites may explain reported error rates. Finally, differences in error severity were noted

across the clinical sub-specialities reported in this research, with only Nephrology and Renal

21

Transplantation where all four categories of error severity were observed. We know that this

is the most complex prescribing sub-specialty.[64] Other research[6] reports more than 7% of

errors as serious or potentially lethal but are not attributable to sub-specialty reducing the

potential for learning.

This study demonstrated the intervention could be considered cost-effective as outlined by

the break-even analysis reported in more detail in Appendix C. Making sense of cost-

effectiveness in a healthcare professions education context is complicated.[65, 66] With

respect to this study, the analysis was approached by comparing the cost of the intervention to

an average cost for a medication error in the first study. Clearly the costs and benefits will

vary across different sub-specialty contexts.

LIMITATIONS

Sample sizes in this study were limited and prescribers allocated to groups based on whether

or not they were able to attend simulations, based on clinical commitments, rather than

randomized. Longitudinal prescribing outcomes from the intervention are unknown.

FURTHER RESEARCH

Future research should adopt a granular data collection method similar to the approach used

in this research. However the labour cost of doing so is not insignificant. Similarly a strength

of the granular data collection has facilitated sub-specialty error rate and error severity

reporting which should also be adopted in future research to best inform targeted safety

interventions. If this intervention is adopted, research should establish the longer-term change

in participant learning, practice and patient safety behaviours; this research provides a

template for how such a study may be designed and implemented in the NHS. Finally, this

22

research involved study sites with written inpatient prescription charts, therefore the

effectiveness of this intervention in prescribing contexts with electronic prescribing systems

also requires further investigation.[67]

CONCLUSION

Simulated clinical encounters with personalised, structured and video-enhanced feedback

using a deliberate practice approach significantly improves the prescribing performance and

patient safety behaviours of junior doctors in Foundation Training. This intervention was

demonstrated to be effective across three different clinical sub-specialties in medicine

(nephrology) and surgery (general and orthopaedics). The intervention is cost effective and

has the potential to reduce the avoidable harm resulting from poor prescribing. The

intervention (simulated clinical encounters with personalised, structured and video-enhanced

feedback using a deliberate practice approach) is an important contribution to the WHO’s

Global Patient Safety challenge, “medication without harm”.

ACKNOWLEDGEMENTS

First, we would like to thank the Leicester Kidney Patient Association for supporting and

participating in Study 1. Second, we would like to thank the patients who participated in

Studies 2 and 3 who were affiliated with the Department of Surgery and Orthopaedics, ULH

NHS Trust. Third, we would like to thank the doctors who participated in the research and

senior clinicians and managers who supported this research at both UHL NHS Trust and

ULH NHS Trust.

FUNDING

23

This work was supported by: first, Health Education England grant number LEI0085 and

Pfizer Limited for an unrestricted medical education grant for the pharmacy team to collect

data through an additional member of staff for Study 1; and, second, the Academic Health

Science Network and Health Education England for financial support for Studies 2 and 3.

The funders were not involved in any stage of the research process including the study

design, writing of this article and the decision to submit this article for publication. RSP’s

time on this research was supported through the NIHR academic clinical lecturership

programme.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the

corresponding author. The data are not publicly available due to privacy or ethical

restrictions.

DECLARATION OF INTEREST

None.

AUTHOR CONTRIBUTIONS

WG and RSP were involved in the conception of the study and obtaining funding. WG,

MWS, MMM, AB, AN, RJ, JS and RSP contributed to the intervention design. WG, MWS,

SW, MMM, ZW, JS and RSP contributed to the study design including data collection and

analysis. All authors contributed to manuscript draft and revisions.

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